{
 "cells": [
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    "## Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling *(ilustrative case study)*\n",
    "### *Geoscientific Model Development preprint*\n",
    "### J. Baño-Medina, R. Manzanas and J. M. Gutiérrez\n",
    "https://doi.org/10.5194/gmd-2019-278\n",
    "\n",
    "GitHub repository at https://github.com/SantanderMetGroup/DeepDownscaling"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "This notebook is part of the companion material to the manuscript entitled *Configuration and Intercomparison of Deep Learning Neural Models for Statistical Downscaling* by *J. Baño-Medina, R. Manzanas and J. M. Gutiérrez*, in *Geoscientific Model Development* (https://doi.org/10.5194/gmd-2019-278). In order to increase research transparency, this document allows to reproduce the results presented in the mentioned paper for one of the analysis undertaken there, the prediction of temperature with a convolutional neural network (CNN hereafter) with 10 feature maps. Note however that the full code needed to reproduce the rest of experiments presented in the paper (including all architectures, standard bencmarking methods and also resutls for precipitation) can be found in the above GitHub repository. \n",
    "\n",
    "**Note:** This notebook is written in the free programming language `R` and builds on the `R` framework [`climate4R`](https://github.com/SantanderMetGroup/climate4R) (C4R hereafter, conda and docker installations available), a suite of `R` packages developed by the [Santander Met Group](http://meteo.unican.es) for transparent climate data access, post processing (including bias correction and downscaling, via the [`downscaleR`](https://github.com/SantanderMetGroup/downscaleR) package; [Bedia et al. 2020](https://www.geosci-model-dev-discuss.net/gmd-2019-224/)) and visualization. The interested reader is referred to [Iturbide et al. 2019](https://www.sciencedirect.com/science/article/pii/S1364815218303049?via%3Dihub). \n",
    "\n",
    "Here we use a **specific version of the conda C4R installer (v1.3.0)** containing the package versions needed to fully reproduce the results of this notebook and including also the [`downscaleR.keras`](https://github.com/SantanderMetGroup/downscaleR.keras) package which is not included in the standard C4R conda distribution (visit this [site](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) for more information on how to obtain conda). Thus, below we illustrate how to create a new environment with conda to install the C4R libraries (please visit the [`climate4R`](https://github.com/SantanderMetGroup/climate4R) github repository for more details regarding the installation procedures). Note that the packages can be also installed individually, but this is more time consuming than using the conda installation."
   ]
  },
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   "execution_count": null,
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   "source": [
    "## --- type this in terminal to proceed with the installation of C4R with conda\n",
    "\n",
    "# conda create --name myEnvironment  # create a new environment\n",
    "# conda activate myEnvironment  # activate the environment\n",
    "## install climate4R from conda v1.3.0 version (includes deep learning tools)\n",
    "# conda install -c conda-forge -c defaults -c santandermetgroup climate4r=1.3.0  \n",
    "# R # type 'R' to initialize R software"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Loading and preparing the data\n",
    "As explained in the paper, we have considered 20 large-scale variables from the ERA-Interim reanalysis as predictors and surface temperature from E-OBS as predictand. All these data can be loaded remotely from the [Santander Climate Data Service](http://meteo.unican.es/cds) (register [here](https://meteo.unican.es/trac/wiki/udg) freely to get a user), which provides access to various kinds of climate datasets (global and regional climate models, reanalysis, observations...). We will use the C4R packages [`loadeR`](https://github.com/SantanderMetGroup/loadeR) and [`transformeR`](https://github.com/SantanderMetGroup/transformeR) to load and postprocess the required information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "button": false,
    "new_sheet": false,
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     "read_only": false
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: rJava\n",
      "Loading required package: loadeR.java\n",
      "Java version 1.8x amd64 by JetBrains s.r.o detected\n",
      "NetCDF Java Library v4.6.0-SNAPSHOT (23 Apr 2015) loaded and ready\n",
      "Loading required package: climate4R.UDG\n",
      "climate4R.UDG version 0.1.0 (2020-02-24) is loaded\n",
      "Please use 'citation(\"climate4R.UDG\")' to cite this package.\n",
      "loadeR version 1.6.1 (2020-03-23) is loaded\n",
      "Please use 'citation(\"loadeR\")' to cite this package.\n",
      "\n",
      "Attaching package: ‘loadeR’\n",
      "\n",
      "The following object is masked from ‘package:climate4R.UDG’:\n",
      "\n",
      "    loginUDG\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    _______   ____  ___________________  __  ________ \n",
      "   / ___/ /  / /  |/  / __  /_  __/ __/ / / / / __  / \n",
      "  / /  / /  / / /|_/ / /_/ / / / / __/ / /_/ / /_/_/  \n",
      " / /__/ /__/ / /  / / __  / / / / /__ /___  / / \\ \\ \n",
      " \\___/____/_/_/  /_/_/ /_/ /_/  \\___/    /_/\\/   \\_\\ \n",
      " \n",
      "      github.com/SantanderMetGroup/climate4R\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "transformeR version 1.7.4 (2020-03-23) is loaded\n",
      "Please see 'citation(\"transformeR\")' to cite this package.\n",
      "Warning message:\n",
      "“'loginUDG' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'loginUDG' from package climate4R.UDG instead.”[2020-03-26 11:04:47] Setting credentials...\n",
      "[2020-03-26 11:04:47] Success!\n",
      "Go to <http://www.meteo.unican.es/udg-tap/home> for details on your authorized groups and datasets\n"
     ]
    }
   ],
   "source": [
    "library(loadeR)      # version 1.6.1 (from conda installation)      \n",
    "library(transformeR) # version 1.7.4 (from conda installation) \n",
    "library(magrittr)    # auxiliary package to simplify the code syntax\n",
    "loginUDG(username = \"\", password = \"\")  # login in the Santander CDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Loading predictors"
   ]
  },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:04:56] Defining harmonization parameters for variable \"z@500\"\n",
      "[2020-03-26 11:04:56] Opening dataset...\n",
      "[2020-03-26 11:04:58] The dataset was successfuly opened\n",
      "[2020-03-26 11:04:58] Defining geo-location parameters\n",
      "[2020-03-26 11:04:58] Defining time selection parameters\n",
      "[2020-03-26 11:04:59] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:05:10] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:05:10] Defining harmonization parameters for variable \"z@700\"\n",
      "[2020-03-26 11:05:11] Opening dataset...\n",
      "[2020-03-26 11:05:11] The dataset was successfuly opened\n",
      "[2020-03-26 11:05:11] Defining geo-location parameters\n",
      "[2020-03-26 11:05:12] Defining time selection parameters\n",
      "[2020-03-26 11:05:12] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:05:25] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:05:25] Defining harmonization parameters for variable \"z@850\"\n",
      "[2020-03-26 11:05:25] Opening dataset...\n",
      "[2020-03-26 11:05:26] The dataset was successfuly opened\n",
      "[2020-03-26 11:05:26] Defining geo-location parameters\n",
      "[2020-03-26 11:05:26] Defining time selection parameters\n",
      "[2020-03-26 11:05:27] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:05:40] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:05:40] Defining harmonization parameters for variable \"z@1000\"\n",
      "[2020-03-26 11:05:40] Opening dataset...\n",
      "[2020-03-26 11:05:41] The dataset was successfuly opened\n",
      "[2020-03-26 11:05:41] Defining geo-location parameters\n",
      "[2020-03-26 11:05:41] Defining time selection parameters\n",
      "[2020-03-26 11:05:41] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:05:55] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:05:55] Defining harmonization parameters for variable \"hus@500\"\n",
      "[2020-03-26 11:05:55] Opening dataset...\n",
      "[2020-03-26 11:05:56] The dataset was successfuly opened\n",
      "[2020-03-26 11:05:56] Defining geo-location parameters\n",
      "[2020-03-26 11:05:56] Defining time selection parameters\n",
      "[2020-03-26 11:05:57] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:06:08] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:06:08] Defining harmonization parameters for variable \"hus@700\"\n",
      "[2020-03-26 11:06:08] Opening dataset...\n",
      "[2020-03-26 11:06:09] The dataset was successfuly opened\n",
      "[2020-03-26 11:06:09] Defining geo-location parameters\n",
      "[2020-03-26 11:06:09] Defining time selection parameters\n",
      "[2020-03-26 11:06:09] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:06:23] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:06:23] Defining harmonization parameters for variable \"hus@850\"\n",
      "[2020-03-26 11:06:23] Opening dataset...\n",
      "[2020-03-26 11:06:24] The dataset was successfuly opened\n",
      "[2020-03-26 11:06:24] Defining geo-location parameters\n",
      "[2020-03-26 11:06:24] Defining time selection parameters\n",
      "[2020-03-26 11:06:25] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:06:37] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:06:38] Defining harmonization parameters for variable \"hus@1000\"\n",
      "[2020-03-26 11:06:38] Opening dataset...\n",
      "[2020-03-26 11:06:38] The dataset was successfuly opened\n",
      "[2020-03-26 11:06:38] Defining geo-location parameters\n",
      "[2020-03-26 11:06:39] Defining time selection parameters\n",
      "[2020-03-26 11:06:39] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:06:52] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:06:52] Defining harmonization parameters for variable \"ta@500\"\n",
      "[2020-03-26 11:06:52] Opening dataset...\n",
      "[2020-03-26 11:06:53] The dataset was successfuly opened\n",
      "[2020-03-26 11:06:53] Defining geo-location parameters\n",
      "[2020-03-26 11:06:53] Defining time selection parameters\n",
      "[2020-03-26 11:06:54] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:07:05] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:07:06] Defining harmonization parameters for variable \"ta@700\"\n",
      "[2020-03-26 11:07:06] Opening dataset...\n",
      "[2020-03-26 11:07:06] The dataset was successfuly opened\n",
      "[2020-03-26 11:07:06] Defining geo-location parameters\n",
      "[2020-03-26 11:07:07] Defining time selection parameters\n",
      "[2020-03-26 11:07:07] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:07:19] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:07:19] Defining harmonization parameters for variable \"ta@850\"\n",
      "[2020-03-26 11:07:19] Opening dataset...\n",
      "[2020-03-26 11:07:20] The dataset was successfuly opened\n",
      "[2020-03-26 11:07:20] Defining geo-location parameters\n",
      "[2020-03-26 11:07:21] Defining time selection parameters\n",
      "[2020-03-26 11:07:21] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:07:33] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:07:33] Defining harmonization parameters for variable \"ta@1000\"\n",
      "[2020-03-26 11:07:33] Opening dataset...\n",
      "[2020-03-26 11:07:33] The dataset was successfuly opened\n",
      "[2020-03-26 11:07:33] Defining geo-location parameters\n",
      "[2020-03-26 11:07:34] Defining time selection parameters\n",
      "[2020-03-26 11:07:34] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:07:49] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:07:49] Defining harmonization parameters for variable \"ua@500\"\n",
      "[2020-03-26 11:07:49] Opening dataset...\n",
      "[2020-03-26 11:07:50] The dataset was successfuly opened\n",
      "[2020-03-26 11:07:50] Defining geo-location parameters\n",
      "[2020-03-26 11:07:50] Defining time selection parameters\n",
      "[2020-03-26 11:07:50] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:08:05] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:08:05] Defining harmonization parameters for variable \"ua@700\"\n",
      "[2020-03-26 11:08:05] Opening dataset...\n",
      "[2020-03-26 11:08:05] The dataset was successfuly opened\n",
      "[2020-03-26 11:08:05] Defining geo-location parameters\n",
      "[2020-03-26 11:08:06] Defining time selection parameters\n",
      "[2020-03-26 11:08:06] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:08:19] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:08:19] Defining harmonization parameters for variable \"ua@850\"\n",
      "[2020-03-26 11:08:19] Opening dataset...\n",
      "[2020-03-26 11:08:20] The dataset was successfuly opened\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2020-03-26 11:08:20] Defining geo-location parameters\n",
      "[2020-03-26 11:08:21] Defining time selection parameters\n",
      "[2020-03-26 11:08:21] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:08:34] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:08:34] Defining harmonization parameters for variable \"ua@1000\"\n",
      "[2020-03-26 11:08:34] Opening dataset...\n",
      "[2020-03-26 11:08:34] The dataset was successfuly opened\n",
      "[2020-03-26 11:08:34] Defining geo-location parameters\n",
      "[2020-03-26 11:08:35] Defining time selection parameters\n",
      "[2020-03-26 11:08:35] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:08:48] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:08:49] Defining harmonization parameters for variable \"va@500\"\n",
      "[2020-03-26 11:08:49] Opening dataset...\n",
      "[2020-03-26 11:08:49] The dataset was successfuly opened\n",
      "[2020-03-26 11:08:49] Defining geo-location parameters\n",
      "[2020-03-26 11:08:50] Defining time selection parameters\n",
      "[2020-03-26 11:08:50] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:09:04] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:09:04] Defining harmonization parameters for variable \"va@700\"\n",
      "[2020-03-26 11:09:04] Opening dataset...\n",
      "[2020-03-26 11:09:05] The dataset was successfuly opened\n",
      "[2020-03-26 11:09:05] Defining geo-location parameters\n",
      "[2020-03-26 11:09:05] Defining time selection parameters\n",
      "[2020-03-26 11:09:05] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:09:19] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:09:19] Defining harmonization parameters for variable \"va@850\"\n",
      "[2020-03-26 11:09:19] Opening dataset...\n",
      "[2020-03-26 11:09:19] The dataset was successfuly opened\n",
      "[2020-03-26 11:09:19] Defining geo-location parameters\n",
      "[2020-03-26 11:09:20] Defining time selection parameters\n",
      "[2020-03-26 11:09:20] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:09:35] Done\n",
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:09:35] Defining harmonization parameters for variable \"va@1000\"\n",
      "[2020-03-26 11:09:35] Opening dataset...\n",
      "[2020-03-26 11:09:36] The dataset was successfuly opened\n",
      "[2020-03-26 11:09:36] Defining geo-location parameters\n",
      "[2020-03-26 11:09:36] Defining time selection parameters\n",
      "[2020-03-26 11:09:36] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:09:50] Done\n"
     ]
    }
   ],
   "source": [
    "## --- the next chunk of code takes about 5 minutes to be completed \n",
    "## --- in a personal computer with the technical specifications given at the end of this notebook\n",
    "\n",
    "## loading predictor variables from ERA-Interim\n",
    "variables <- c(\"z@500\",\"z@700\",\"z@850\",\"z@1000\",  \n",
    "               \"hus@500\",\"hus@700\",\"hus@850\",\"hus@1000\",\n",
    "               \"ta@500\",\"ta@700\",\"ta@850\",\"ta@1000\",\n",
    "               \"ua@500\",\"ua@700\",\"ua@850\",\"ua@1000\",\n",
    "               \"va@500\",\"va@700\",\"va@850\",\"va@1000\")\n",
    "x <- lapply(variables, function(x) {\n",
    "  loadGridData(dataset = \"ECMWF_ERA-Interim-ESD\",\n",
    "               var = x,\n",
    "               lonLim = c(-10, 32), # longitude domain: 22 gridboxes (2º resolution)\n",
    "               latLim = c(36, 72),  # latitude domain: 19 gridboxes (2º resolution)\n",
    "               years = 1979:2008)  # total period of study\n",
    "}) %>% makeMultiGrid() %>% redim(, drop = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
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    "run_control": {
     "read_only": false
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   },
   "source": [
    "### Loading predictand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "NOTE: Accessing harmonized data from a public UDG dataset\n",
      "[2020-03-26 11:11:18] Defining harmonization parameters for variable \"tas\"\n",
      "[2020-03-26 11:11:18] Opening dataset...\n",
      "[2020-03-26 11:11:19] The dataset was successfuly opened\n",
      "[2020-03-26 11:11:19] Defining geo-location parameters\n",
      "[2020-03-26 11:11:19] Defining time selection parameters\n",
      "[2020-03-26 11:11:19] Retrieving data subset ...\n",
      "Warning message:\n",
      "“'C4R.vocabulary' is deprecated and will eventually be removed from loadeR.\n",
      "Use 'C4R.vocabulary' from package climate4R.UDG instead.”[2020-03-26 11:12:07] Done\n"
     ]
    }
   ],
   "source": [
    "## --- the next chunk of code takes about 1 minute to be completed \n",
    "## --- in a personal computer with the technical specifications given at the end of this notebook\n",
    "\n",
    "# loading predictand (temperature), from E-OBS\n",
    "y <- loadGridData(dataset = \"E-OBS_v14_0.50regular\",\n",
    "                   var = \"tas\",\n",
    "                   lonLim = c(-10, 32), # longitude domain: 85 gridboxes (0.5º resolution)\n",
    "                   latLim = c(36, 72),  # latitude domain: 73 gridboxes (0.5º resolution)\n",
    "                   years = 1979:2008)  # total period of study"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Train/test partition\n",
    "As in the paper, we split the entire period of study into *train* (1979-2002) and *test* (2003-2008). To do that, we use the `subsetGrid` function from `transformeR`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "## predictor and predictand data for the train period\n",
    "xT <- subsetGrid(x, years = 1979:2002)\n",
    "yT <- subsetGrid(y, years = 1979:2002)\n",
    "\n",
    "## predictor and predictand data for the test period\n",
    "xt <- subsetGrid(x ,years = 2003:2008)\n",
    "yt <- subsetGrid(y ,years = 2003:2008)\n",
    "\n",
    "rm(x, y)  # to save memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Data standardization\n",
    "The predictors are then standardized at a gridbox level using the function `scaleGrid` (also from `transformeR`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2020-03-26 11:14:21] - Scaling ...\n",
      "[2020-03-26 11:15:22] - Done\n",
      "[2020-03-26 11:15:22] - Scaling ...\n",
      "[2020-03-26 11:16:15] - Done\n"
     ]
    }
   ],
   "source": [
    "# test period: standardization is done with respect to the train period\n",
    "xt <- scaleGrid(xt, xT, type = \"standardize\", spatial.frame = \"gridbox\") %>% redim(drop = TRUE) \n",
    "# train period\n",
    "xT <- scaleGrid(xT, type = \"standardize\", spatial.frame = \"gridbox\") %>% redim(drop = TRUE)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Downscaling\n",
    "Once the predictors and the predictand are loaded, we can start the downscaling process. We rely on the [`C4R`](https://github.com/SantanderMetGroup/climate4R) library [`downscaleR.keras`](https://github.com/SantanderMetGroup/downscaleR.keras) which builds on [`keras`](https://cran.r-project.org/web/packages/keras/index.html), a high-level neural networks API which supports arbitrary network architectures and is seamlessly integrated with [TensorFlow](https://www.tensorflow.org/) (note that keras and TensorFlow are the state of the art in deep learning tools)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: keras\n",
      "Loading required package: tensorflow\n"
     ]
    }
   ],
   "source": [
    "library(downscaleR.keras)  # version 0.0.2 (relies on keras version 2.2.2 and tensorflow version 2.0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We call the functions `prepareData.keras` and `prepareNewData.keras` to adequate the shape of predictor and predictand datasets to the keras format. This includes reshaping and resizing according to the first and last connections, which can be convolutional (\"conv\") or fully connected (\"dense\"). Moreover, this function discards all the gridboxes over the sea in the E-OBS dataset, for which there are no data; and also those land points for which some missing data is present (`keras` does not support missing values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in prepareData.keras(xT, yT, first.connection = \"conv\", last.connection = \"dense\", :\n",
      "“removing gridpoints containing NaNs of object: y”"
     ]
    }
   ],
   "source": [
    "xy.T <- prepareData.keras(xT,yT,\n",
    "                          first.connection = \"conv\",\n",
    "                          last.connection = \"dense\",\n",
    "                          channels = \"last\")\n",
    "xy.t <- prepareNewData.keras(xt,xy.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Network architecture\n",
    "Next, we define the architecture of our network (labelled as CNN10 in the paper; see Table 2). This network includes a block of three convolutional layers with 50, 25 and 10 (3 x 3 x number of inputs coming from the previous layer) kernels, respectively. In the hidden layers, the *ReLu* activation function is used, and padding is considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "## defining the network architecture in keras\n",
    "inputs <- layer_input(shape = dim(xy.T$x.global)[-1])\n",
    "x = inputs # inputs\n",
    "l1 = layer_conv_2d(x ,filters = 50, kernel_size = c(3,3), activation = 'relu', padding = \"valid\")# first layer\n",
    "l2 = layer_conv_2d(l1,filters = 25, kernel_size = c(3,3), activation = 'relu', padding = \"valid\")# second layer\n",
    "l3 = layer_conv_2d(l2,filters = 10, kernel_size = c(3,3), activation = 'relu', padding = \"valid\")# third layer\n",
    "l4 = layer_flatten(l3)\n",
    "outputs = layer_dense(l4,units = dim(xy.T$y$Data)[2]) # outputs\n",
    "model <- keras_model(inputs = inputs, outputs = outputs) # network architecture"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Training the network\n",
    "We are now in the position to compile and train the network. To do so, we use the function `downscaleTrain.keras` of `downscaleR.keras`. Note that, in terms of computation, this is the most expensive step (it took 3 hours in the machine used to write this notebook). \n",
    "Despite the parameter `epochs` is set to 10000 in the following chunk of code, early-stopping is applied with a patience of 30 epochs. Therefore, the training ends whenever the error in the validation dataset (10% of the training period, as indicated by `validation_split = 0.1`) stops decreasing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "## --- the next chunk of code takes about 3 hours to be completed \n",
    "## --- in a personal computer with the technical specifications given at the end of this notebook\n",
    "\n",
    "model <- downscaleTrain.keras(obj = xy.T,\n",
    "                       model = model,\n",
    "                       clear.session = FALSE,\n",
    "                       compile.args = list(\"loss\" = \"mse\",\n",
    "                                           \"optimizer\" = optimizer_adam(lr = 0.0001)),\n",
    "                       fit.args = list(\"batch_size\" = 100,\n",
    "                                       \"epochs\" = 1000,\n",
    "                                       \"validation_split\" = 0.1,\n",
    "                                       \"verbose\" = 1,\n",
    "                                       \"callbacks\" = list(callback_early_stopping(patience = 30))))\n",
    "## --- stopped at epoch 431, with loss: 0.8510, and val_loss: 1.0944"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Making predictions\n",
    "Once the network has been successfully trained, we can use it to make our predictions. To do so we use the `downscalePredict.keras` function of `downscaleR.keras`. The previously defined `xy.t` object contains the new predictor data and we use the `model` to predict on this dataset. We use the argument `clear.session = TRUE` to remove the model and the `C4R.template` argument as a template where to locate the output neurons in the 2D latitude-longitude map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in makeMultiGrid(.):\n",
      "“One single grid was provided as input”NOTE: One single grid passed to the function: nothing to bind, so the original grid was returned\n"
     ]
    }
   ],
   "source": [
    "## predict on the test set\n",
    "pred <- downscalePredict.keras(newdata = xy.t,\n",
    "                               model = model,\n",
    "                               C4R.template = yT,\n",
    "                               clear.session = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Validation of results\n",
    "Finally, the predictions which have been just created (and saved) are loaded and validated. Here we focus on the mean error obtained with respect to the observed cimatological value (bias) and the observed extreme percentiles 2th and 98th (biasP02 and biasP98). These metrics are computed using the function `valueMeasure` from the [`climate4R.value`](https://github.com/SantanderMetGroup/climate4R.value) package, a wrapper to the [`VALUE`](https://github.com/SantanderMetGroup/VALUE) package, which was developed in the [COST action VALUE](http://www.value-cost.eu/) for validation purposes. To plot the final maps (comparable to those shown in the bottom row of Figure 5 in the paper) we also need to load the [`visualizeR`](https://github.com/SantanderMetGroup/visualizeR) and `RColorBrewer` packages. If interested in knowing more about the plotting options used here, the reader is referred to the documentation of the `spatialPlot` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: VALUE\n",
      "---------------------------------------------- \n",
      "| VALUE version 2.2.0 (2019-12-09) is loaded |\n",
      "|         http://www.value-cost.eu           |\n",
      "----------------------------------------------\n",
      "WARNING: Your current version of VALUE (v2.2.0) is not up-to-date\n",
      "Get the latest stable version (2.2.1) using <devtools::install_github('SantanderMetGroup/VALUE')>\n",
      "Warning message:\n",
      "“no DISPLAY variable so Tk is not available”visualizeR version 1.5.1 (2020-01-05) is loaded\n",
      "Please see 'citation(\"visualizeR\")' to cite this package.\n",
      "[2020-03-26 12:17:13] Computing member 1 out of 1\n",
      "[2020-03-26 12:17:44] Done.\n",
      "[2020-03-26 12:17:47] Computing member 1 out of 1\n",
      "[2020-03-26 12:18:21] Done.\n",
      "[2020-03-26 12:18:26] Computing member 1 out of 1\n",
      "[2020-03-26 12:18:51] Done.\n",
      "[2020-03-26 12:18:55] Computing member 1 out of 1\n",
      "[2020-03-26 12:19:32] Done.\n",
      "[2020-03-26 12:19:35] Computing member 1 out of 1\n",
      "[2020-03-26 12:20:11] Done.\n",
      "[2020-03-26 12:20:14] Computing member 1 out of 1\n",
      "[2020-03-26 12:20:38] Done.\n",
      "[2020-03-26 12:20:42] Computing member 1 out of 1\n",
      "[2020-03-26 12:21:17] Done.\n",
      "[2020-03-26 12:21:21] Computing member 1 out of 1\n",
      "[2020-03-26 12:21:53] Done.\n",
      "[2020-03-26 12:21:57] Computing member 1 out of 1\n",
      "[2020-03-26 12:22:19] Done.\n",
      "[2020-03-26 12:22:19] - Computing climatology...\n",
      "[2020-03-26 12:22:19] - Done.\n",
      "[2020-03-26 12:22:19] - Computing climatology...\n",
      "[2020-03-26 12:22:20] - Done.\n",
      "[2020-03-26 12:22:20] - Computing climatology...\n",
      "[2020-03-26 12:22:20] - Done.\n"
     ]
    },
    {
     "data": {
      "image/png": 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ojZYwe3//z7gFa9+/f4pIxH8YjHkcs27HgWHbtw6vfyzI6c2+85gp1xSNfMf/t5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\nXTz617WgA+5e5Z/HJ+Xkv5FyEeyQJbfv3J2/aMmaDRstLCyqVK5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190m8R6eZo0+YfhO49aWrefoWkRoD6peRGuu/1I37louVcmmySMjf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\n4t9zp3v3M2exZB3Gj8rLy71zK6Le1YrSz5mfPmXt2X/g0BG/oKAg0W9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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## --- the next chunk of code takes about 2 minutes be completed \n",
    "## --- in a personal computer with the technical specifications given at the end of this notebook\n",
    "\n",
    "## loading packages\n",
    "library(climate4R.value) # version 0.0.2 (also relies on VALUE version 2.2.1)\n",
    "library(visualizeR)\n",
    "library(RColorBrewer)  # for color palettes\n",
    "\n",
    "## computing validation metrics\n",
    "bias <- valueMeasure(yt, pred, measure.code = \"bias\", index.code = \"Mean\")$Measure %>% redim()\n",
    "biasP02 <- valueMeasure(yt, pred, measure.code = \"bias\", index.code = \"P02\")$Measure %>% redim()\n",
    "biasP98 <- valueMeasure(yt, pred, measure.code = \"bias\", index.code = \"P98\")$Measure %>% redim()\n",
    "\n",
    "## producing (and saving) final figure, in pdf format\n",
    "#pdf(file = paste0(dir.figs, \"/\", architecture, \"_bias_test.pdf\"))  # output file. uncomment this line if wants to directly save the figure to a specific location\n",
    "print(spatialPlot(makeMultiGrid(climatology(bias),\n",
    "                          climatology(biasP02),\n",
    "                          climatology(biasP98)),  \n",
    "            backdrop.theme = \"coastline\",\n",
    "            layout = c(3, 1), as.table = T,\n",
    "            main = \"CNN10\",  # architechture\n",
    "            names.attr = c(\"bias\", \"bias (P02)\", \"bias (P98)\"),\n",
    "            col.regions = colorRampPalette(rev(brewer.pal(n = 9, \"RdBu\"))),\n",
    "            set.min = -2, set.max = 2, at = seq(-2, 2, 0.1)))\n",
    "#dev.off()   # uncomment this line if wants to directly save the figure to a specific location"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
